{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ROSg9T9EzYDf"
   },
   "source": [
    "# Intent Recognition with BERT using Keras and TensorFlow 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "WaaST8ILn0oN",
    "outputId": "7ebaa051-b1c7-4ee4-f757-b848a0b05f22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow==2.2.0-rc1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (2.2.0rc1)\n",
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      "Requirement already satisfied: tensorflow==2.2.0-rc1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (2.2.0rc1)\n",
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      "Requirement already satisfied: tensorboard<2.2.0,>=2.1.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (2.1.1)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.27.2)\n",
      "Requirement already satisfied: astunparse==1.6.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.6.3)\n",
      "Requirement already satisfied: numpy<2.0,>=1.16.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.18.2)\n",
      "Requirement already satisfied: protobuf>=3.8.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (3.11.3)\n",
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      "Requirement already satisfied: keras-preprocessing>=1.1.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.1.0)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.12.1)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.1.0)\n",
      "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.4.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.8 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (0.2.0)\n",
      "Requirement already satisfied: gast==0.3.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (0.3.3)\n",
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      "Requirement already satisfied: astunparse==1.6.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.6.3)\n",
      "Requirement already satisfied: numpy<2.0,>=1.16.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.18.2)\n",
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      "Requirement already satisfied: six>=1.12.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.14.0)\n",
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      "Requirement already satisfied: keras-preprocessing>=1.1.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.1.0)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.12.1)\n",
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      "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (1.4.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.8 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (0.2.0)\n",
      "Requirement already satisfied: gast==0.3.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (0.3.3)\n",
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      "Requirement already satisfied: opt-einsum>=2.3.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorflow==2.2.0-rc1) (3.2.0)\n",
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      "Requirement already satisfied: markdown>=2.6.8 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (3.2.1)\n",
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      "Requirement already satisfied: werkzeug>=0.11.15 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (0.14.1)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (46.1.1)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (2.6)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (2019.11.28)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (1.23)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (0.2.8)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (4.0.0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: markdown>=2.6.8 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (3.2.1)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (1.11.3)\n",
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      "Requirement already satisfied: werkzeug>=0.11.15 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (0.14.1)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (46.1.1)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (2.6)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (2019.11.28)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (1.23)\n",
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      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (4.0.0)\n",
      "Requirement already satisfied: rsa<4.1,>=3.1.4 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (3.4.2)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (1.3.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (3.1.0)\n",
      "Requirement already satisfied: rsa<4.1,>=3.1.4 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (3.4.2)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (1.3.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.2.0-rc1) (3.1.0)\n",
      "Requirement already up-to-date: grpcio in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (1.27.2)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from grpcio) (1.14.0)\n",
      "Requirement already up-to-date: grpcio in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (1.27.2)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5.2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from grpcio) (1.14.0)\n",
      "Requirement already satisfied: tqdm in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (4.43.0)\n",
      "Requirement already satisfied: tqdm in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (4.43.0)\n",
      "Requirement already satisfied: bert-for-tf2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (0.13.5)\n",
      "Requirement already satisfied: py-params>=0.7.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from bert-for-tf2) (0.8.3)\n",
      "Requirement already satisfied: params-flow>=0.7.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from bert-for-tf2) (0.7.4)\n",
      "Requirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from params-flow>=0.7.1->bert-for-tf2) (1.18.2)\n",
      "Requirement already satisfied: tqdm in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from params-flow>=0.7.1->bert-for-tf2) (4.43.0)\n",
      "Requirement already satisfied: bert-for-tf2 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (0.13.5)\n",
      "Requirement already satisfied: py-params>=0.7.3 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from bert-for-tf2) (0.8.3)\n",
      "Requirement already satisfied: params-flow>=0.7.1 in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from bert-for-tf2) (0.7.4)\n",
      "Requirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from params-flow>=0.7.1->bert-for-tf2) (1.18.2)\n",
      "Requirement already satisfied: tqdm in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (from params-flow>=0.7.1->bert-for-tf2) (4.43.0)\n",
      "Requirement already satisfied: sentencepiece in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (0.1.85)\n",
      "Requirement already satisfied: sentencepiece in /home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages (0.1.85)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow==2.2.0-rc1\n",
    "!pip install --upgrade grpcio\n",
    "!pip install tqdm\n",
    "!pip install bert-for-tf2\n",
    "!pip install sentencepiece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0pHNG_2SmBOY",
    "outputId": "db784a16-e0d8-4517-ccc8-c69364886b0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0-rc1\n",
      "2.2.0-rc1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "import pandas as pd\n",
    "\n",
    "sess   = sagemaker.Session()\n",
    "bucket = sess.default_bucket()\n",
    "role = sagemaker.get_execution_role()\n",
    "region = boto3.Session().region_name\n",
    "\n",
    "sm = boto3.Session().client(service_name='sagemaker', region_name=region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VLPwBaua6jf2"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import datetime\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import bert\n",
    "from bert import BertModelLayer\n",
    "from bert.loader import StockBertConfig, map_stock_config_to_params, load_stock_weights\n",
    "from bert.tokenization.bert_tokenization import FullTokenizer\n",
    "\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib import rc\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "\n",
    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
    "\n",
    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
    "\n",
    "rcParams['figure.figsize'] = 12, 8\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "\n",
    "np.random.seed(RANDOM_SEED)\n",
    "tf.random.set_seed(RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CzkSXum8RzGM"
   },
   "source": [
    "# Data\n",
    "\n",
    "The data contains various user queries categorized into seven intents. It is hosted on [GitHub](https://github.com/snipsco/nlu-benchmark/tree/master/2017-06-custom-intent-engines) and is first presented in [this paper](https://arxiv.org/abs/1805.10190)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r scikit_processing_job_s3_output_prefix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous Scikit Processing Job Name: sagemaker-scikit-learn-2020-03-27-05-28-30-227\n",
      "Previous Scikit Processing Job Name: sagemaker-scikit-learn-2020-03-27-05-28-30-227\n"
     ]
    }
   ],
   "source": [
    "print('Previous Scikit Processing Job Name: {}'.format(scikit_processing_job_s3_output_prefix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "eL2BmRgYIZkl",
    "outputId": "40fbab20-4a96-4344-c022-ffc8b5b0d8e7"
   },
   "outputs": [],
   "source": [
    "# prefix_train = '{}/output/bert-train'.format(scikit_processing_job_s3_output_prefix)\n",
    "# prefix_validation = '{}/output/bert-validation'.format(scikit_processing_job_s3_output_prefix)\n",
    "# prefix_test = '{}/output/bert-test'.format(scikit_processing_job_s3_output_prefix)\n",
    "\n",
    "# path_train = './{}'.format(prefix_train)\n",
    "# path_validation = './{}'.format(prefix_validation)\n",
    "# path_test = './{}'.format(prefix_test)\n",
    "\n",
    "# train_s3_uri = 's3://{}/{}'.format(bucket, prefix_train)\n",
    "# validation_s3_uri = 's3://{}/{}'.format(bucket, prefix_validation)\n",
    "# test_s3_uri = 's3://{}/{}'.format(bucket, prefix_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s3_input_train_data = sagemaker.s3_input(s3_data=train_s3_uri) #, content_type='text/csv')\n",
    "# s3_input_validation_data = sagemaker.s3_input(s3_data=validation_s3_uri) #, content_type='text/csv')\n",
    "# s3_input_test_data = sagemaker.s3_input(s3_data=test_s3_uri) #, content_type='text/csv')\n",
    "\n",
    "# print(s3_input_train_data.config)\n",
    "# print(s3_input_validation_data.config)\n",
    "# print(s3_input_test_data.config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !aws s3 cp --recursive $train_s3_uri $path_train\n",
    "# !aws s3 cp --recursive $validation_s3_uri $path_validation\n",
    "# !aws s3 cp --recursive $test_s3_uri $path_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5RNKtsTSTPhh"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('./data/amazon_reviews_us_Digital_Software_v1_00.tsv.gz', delimiter='\\t')[:100]\n",
    "#valid = pd.read_csv(\"valid.csv\")\n",
    "test = pd.read_csv('./data/amazon_reviews_us_Digital_Software_v1_00.tsv.gz', delimiter='\\t')[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nw4hUCMgTdmE"
   },
   "outputs": [],
   "source": [
    "#train = train.append(valid).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "VsILJQbMgGlt",
    "outputId": "65253b8c-888e-4c94-c453-42dfcf287c99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 15)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(100, 15)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "i4cZ4oi_TTfC",
    "outputId": "f397dae8-1c43-481c-8763-469d06de2f35"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marketplace</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_parent</th>\n",
       "      <th>product_title</th>\n",
       "      <th>product_category</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_headline</th>\n",
       "      <th>review_body</th>\n",
       "      <th>review_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>17747349</td>\n",
       "      <td>R2EI7QLPK4LF7U</td>\n",
       "      <td>B00U7LCE6A</td>\n",
       "      <td>106182406</td>\n",
       "      <td>CCleaner Free [Download]</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Four Stars</td>\n",
       "      <td>So far so good</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>10956619</td>\n",
       "      <td>R1W5OMFK1Q3I3O</td>\n",
       "      <td>B00HRJMOM4</td>\n",
       "      <td>162269768</td>\n",
       "      <td>ResumeMaker Professional Deluxe 18</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Three Stars</td>\n",
       "      <td>Needs a little more work.....</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>13132245</td>\n",
       "      <td>RPZWSYWRP92GI</td>\n",
       "      <td>B00P31G9PQ</td>\n",
       "      <td>831433899</td>\n",
       "      <td>Amazon Drive Desktop [PC]</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>One Star</td>\n",
       "      <td>Please cancel.</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>35717248</td>\n",
       "      <td>R2WQWM04XHD9US</td>\n",
       "      <td>B00FGDEPDY</td>\n",
       "      <td>991059534</td>\n",
       "      <td>Norton Internet Security 1 User 3 Licenses</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Works as Expected!</td>\n",
       "      <td>Works as Expected!</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>17710652</td>\n",
       "      <td>R1WSPK2RA2PDEF</td>\n",
       "      <td>B00FZ0FK0U</td>\n",
       "      <td>574904556</td>\n",
       "      <td>SecureAnywhere Intermet Security Complete 5 De...</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Great antivirus. Worthless customer support</td>\n",
       "      <td>I've had Webroot for a few years. It expired a...</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  marketplace  customer_id       review_id  product_id  product_parent  \\\n",
       "0          US     17747349  R2EI7QLPK4LF7U  B00U7LCE6A       106182406   \n",
       "1          US     10956619  R1W5OMFK1Q3I3O  B00HRJMOM4       162269768   \n",
       "2          US     13132245   RPZWSYWRP92GI  B00P31G9PQ       831433899   \n",
       "3          US     35717248  R2WQWM04XHD9US  B00FGDEPDY       991059534   \n",
       "4          US     17710652  R1WSPK2RA2PDEF  B00FZ0FK0U       574904556   \n",
       "\n",
       "                                       product_title  product_category  \\\n",
       "0                           CCleaner Free [Download]  Digital_Software   \n",
       "1                 ResumeMaker Professional Deluxe 18  Digital_Software   \n",
       "2                          Amazon Drive Desktop [PC]  Digital_Software   \n",
       "3         Norton Internet Security 1 User 3 Licenses  Digital_Software   \n",
       "4  SecureAnywhere Intermet Security Complete 5 De...  Digital_Software   \n",
       "\n",
       "   star_rating  helpful_votes  total_votes vine verified_purchase  \\\n",
       "0            4              0            0    N                 Y   \n",
       "1            3              0            0    N                 Y   \n",
       "2            1              1            2    N                 Y   \n",
       "3            5              0            0    N                 Y   \n",
       "4            4              1            2    N                 Y   \n",
       "\n",
       "                               review_headline  \\\n",
       "0                                   Four Stars   \n",
       "1                                  Three Stars   \n",
       "2                                     One Star   \n",
       "3                           Works as Expected!   \n",
       "4  Great antivirus. Worthless customer support   \n",
       "\n",
       "                                         review_body review_date  \n",
       "0                                     So far so good  2015-08-31  \n",
       "1                      Needs a little more work.....  2015-08-31  \n",
       "2                                     Please cancel.  2015-08-31  \n",
       "3                                 Works as Expected!  2015-08-31  \n",
       "4  I've had Webroot for a few years. It expired a...  2015-08-31  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marketplace</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_parent</th>\n",
       "      <th>product_title</th>\n",
       "      <th>product_category</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_headline</th>\n",
       "      <th>review_body</th>\n",
       "      <th>review_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>17747349</td>\n",
       "      <td>R2EI7QLPK4LF7U</td>\n",
       "      <td>B00U7LCE6A</td>\n",
       "      <td>106182406</td>\n",
       "      <td>CCleaner Free [Download]</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Four Stars</td>\n",
       "      <td>So far so good</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>10956619</td>\n",
       "      <td>R1W5OMFK1Q3I3O</td>\n",
       "      <td>B00HRJMOM4</td>\n",
       "      <td>162269768</td>\n",
       "      <td>ResumeMaker Professional Deluxe 18</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Three Stars</td>\n",
       "      <td>Needs a little more work.....</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>13132245</td>\n",
       "      <td>RPZWSYWRP92GI</td>\n",
       "      <td>B00P31G9PQ</td>\n",
       "      <td>831433899</td>\n",
       "      <td>Amazon Drive Desktop [PC]</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>One Star</td>\n",
       "      <td>Please cancel.</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>35717248</td>\n",
       "      <td>R2WQWM04XHD9US</td>\n",
       "      <td>B00FGDEPDY</td>\n",
       "      <td>991059534</td>\n",
       "      <td>Norton Internet Security 1 User 3 Licenses</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Works as Expected!</td>\n",
       "      <td>Works as Expected!</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>17710652</td>\n",
       "      <td>R1WSPK2RA2PDEF</td>\n",
       "      <td>B00FZ0FK0U</td>\n",
       "      <td>574904556</td>\n",
       "      <td>SecureAnywhere Intermet Security Complete 5 De...</td>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Great antivirus. Worthless customer support</td>\n",
       "      <td>I've had Webroot for a few years. It expired a...</td>\n",
       "      <td>2015-08-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  marketplace  customer_id       review_id  product_id  product_parent  \\\n",
       "0          US     17747349  R2EI7QLPK4LF7U  B00U7LCE6A       106182406   \n",
       "1          US     10956619  R1W5OMFK1Q3I3O  B00HRJMOM4       162269768   \n",
       "2          US     13132245   RPZWSYWRP92GI  B00P31G9PQ       831433899   \n",
       "3          US     35717248  R2WQWM04XHD9US  B00FGDEPDY       991059534   \n",
       "4          US     17710652  R1WSPK2RA2PDEF  B00FZ0FK0U       574904556   \n",
       "\n",
       "                                       product_title  product_category  \\\n",
       "0                           CCleaner Free [Download]  Digital_Software   \n",
       "1                 ResumeMaker Professional Deluxe 18  Digital_Software   \n",
       "2                          Amazon Drive Desktop [PC]  Digital_Software   \n",
       "3         Norton Internet Security 1 User 3 Licenses  Digital_Software   \n",
       "4  SecureAnywhere Intermet Security Complete 5 De...  Digital_Software   \n",
       "\n",
       "   star_rating  helpful_votes  total_votes vine verified_purchase  \\\n",
       "0            4              0            0    N                 Y   \n",
       "1            3              0            0    N                 Y   \n",
       "2            1              1            2    N                 Y   \n",
       "3            5              0            0    N                 Y   \n",
       "4            4              1            2    N                 Y   \n",
       "\n",
       "                               review_headline  \\\n",
       "0                                   Four Stars   \n",
       "1                                  Three Stars   \n",
       "2                                     One Star   \n",
       "3                           Works as Expected!   \n",
       "4  Great antivirus. Worthless customer support   \n",
       "\n",
       "                                         review_body review_date  \n",
       "0                                     So far so good  2015-08-31  \n",
       "1                      Needs a little more work.....  2015-08-31  \n",
       "2                                     Please cancel.  2015-08-31  \n",
       "3                                 Works as Expected!  2015-08-31  \n",
       "4  I've had Webroot for a few years. It expired a...  2015-08-31  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 596
    },
    "colab_type": "code",
    "id": "Fes5nupOfR9B",
    "outputId": "9570872f-9463-479c-af93-e5fc24a17106"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 388
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 388
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = sns.countplot(train.star_rating, palette=HAPPY_COLORS_PALETTE)\n",
    "plt.title(\"Breakdown of Star Ratings for Reviews\")\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=30, horizontalalignment='right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nc23faMAR--V"
   },
   "source": [
    "# Multi-Class Classification with BERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "-P2y2g3m8Ytp",
    "outputId": "8a6ba7c7-00ac-4cfb-8b63-99e846bbad61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-03-28 06:30:40--  https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip\n",
      "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.13.240, 2607:f8b0:4004:804::2010\n",
      "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.13.240|:443... connected.\n",
      "HTTP request sent, awaiting response... --2020-03-28 06:30:40--  https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip\n",
      "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.13.240, 2607:f8b0:4004:804::2010\n",
      "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.13.240|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 407727028 (389M) [application/zip]\n",
      "Saving to: ‘uncased_L-12_H-768_A-12.zip’\n",
      "\n",
      "          uncased_L   0%[                    ]       0  --.-KB/s               200 OK\n",
      "Length: 407727028 (389M) [application/zip]\n",
      "Saving to: ‘uncased_L-12_H-768_A-12.zip’\n",
      "\n",
      "uncased_L-12_H-768_ 100%[===================>] 388.84M   204MB/s    in 1.9s    \n",
      "\n",
      "2020-03-28 06:30:43 (204 MB/s) - ‘uncased_L-12_H-768_A-12.zip’ saved [407727028/407727028]\n",
      "\n",
      "uncased_L-12_H-768_ 100%[===================>] 388.84M   204MB/s    in 1.9s    \n",
      "\n",
      "2020-03-28 06:30:43 (204 MB/s) - ‘uncased_L-12_H-768_A-12.zip’ saved [407727028/407727028]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "ARivu75M8fqM",
    "outputId": "3f681782-7635-46b6-fe94-96a2bda49d0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  uncased_L-12_H-768_A-12.zip\n",
      "Archive:  uncased_L-12_H-768_A-12.zip\n",
      "total 431244\n",
      "drwxr-xr-x 2 ec2-user ec2-user      4096 Nov  1  2018 .\n",
      "drwxrwxr-x 3 ec2-user ec2-user      4096 Mar 28 05:38 ..\n",
      "-rw-r--r-- 1 ec2-user ec2-user       313 Oct 18  2018 bert_config.json\n",
      "-rw-r--r-- 1 ec2-user ec2-user 440425712 Oct 18  2018 bert_model.ckpt.data-00000-of-00001\n",
      "-rw-r--r-- 1 ec2-user ec2-user      8528 Oct 18  2018 bert_model.ckpt.index\n",
      "-rw-r--r-- 1 ec2-user ec2-user    904243 Oct 18  2018 bert_model.ckpt.meta\n",
      "-rw-r--r-- 1 ec2-user ec2-user    231508 Oct 18  2018 vocab.txt\n",
      "total 431244\n",
      "drwxr-xr-x 2 ec2-user ec2-user      4096 Nov  1  2018 .\n",
      "drwxrwxr-x 3 ec2-user ec2-user      4096 Mar 28 05:38 ..\n",
      "-rw-r--r-- 1 ec2-user ec2-user       313 Oct 18  2018 bert_config.json\n",
      "-rw-r--r-- 1 ec2-user ec2-user 440425712 Oct 18  2018 bert_model.ckpt.data-00000-of-00001\n",
      "-rw-r--r-- 1 ec2-user ec2-user      8528 Oct 18  2018 bert_model.ckpt.index\n",
      "-rw-r--r-- 1 ec2-user ec2-user    904243 Oct 18  2018 bert_model.ckpt.meta\n",
      "-rw-r--r-- 1 ec2-user ec2-user    231508 Oct 18  2018 vocab.txt\n"
     ]
    }
   ],
   "source": [
    "!unzip -f uncased_L-12_H-768_A-12.zip\n",
    "!ls -al ./model/uncased_L-12_H-768_A-12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LbvjbsEb9Ndv"
   },
   "outputs": [],
   "source": [
    "bert_ckpt_dir = './model/uncased_L-12_H-768_A-12'\n",
    "bert_ckpt_file = os.path.join(bert_ckpt_dir, \"bert_model.ckpt\")\n",
    "bert_config_file = os.path.join(bert_ckpt_dir, \"bert_config.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3os6qeC6SB-M"
   },
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TApTW_wLxoA9"
   },
   "outputs": [],
   "source": [
    "class ClassificationData:\n",
    "  TEXT_COLUMN = 'review_body'\n",
    "  LABEL_COLUMN = 'star_rating'\n",
    "\n",
    "  def __init__(self, train, test, tokenizer: FullTokenizer, classes, max_seq_len=192):\n",
    "    self.tokenizer = tokenizer\n",
    "    self.max_seq_len = 0\n",
    "    self.classes = classes\n",
    "    \n",
    "    ((self.train_x, self.train_y), (self.test_x, self.test_y)) = map(self._prepare, [train, test])\n",
    "\n",
    "#    print('max seq_len', self.max_seq_len)\n",
    "    self.max_seq_len = min(self.max_seq_len, max_seq_len)\n",
    "    self.train_x, self.test_x = map(self._pad, [self.train_x, self.test_x])\n",
    "\n",
    "  def _prepare(self, df):\n",
    "    x, y = [], []\n",
    "    \n",
    "    for _, row in tqdm(df.iterrows()):\n",
    "      text, label = row[ClassificationData.TEXT_COLUMN], row[ClassificationData.LABEL_COLUMN]\n",
    "      tokens = self.tokenizer.tokenize(text)\n",
    "      tokens = [\"[CLS]\"] + tokens + [\"[SEP]\"]\n",
    "      token_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n",
    "      self.max_seq_len = max(self.max_seq_len, len(token_ids))\n",
    "      x.append(token_ids)\n",
    "      y.append(self.classes.index(label))\n",
    "\n",
    "    return np.array(x), np.array(y)\n",
    "\n",
    "  def _pad(self, ids):\n",
    "    x = []\n",
    "    for input_ids in ids:\n",
    "      input_ids = input_ids[:min(len(input_ids), self.max_seq_len - 2)]\n",
    "      input_ids = input_ids + [0] * (self.max_seq_len - len(input_ids))\n",
    "      x.append(np.array(input_ids))\n",
    "    return np.array(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qQWpVG464BuO"
   },
   "outputs": [],
   "source": [
    "tokenizer = FullTokenizer(vocab_file=os.path.join(bert_ckpt_dir, \"vocab.txt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9gjqzvNF9JFF",
    "outputId": "d302c799-ceee-45c5-fe33-c1427d67c89b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'can', \"'\", 't', 'wait', 'to', 'visit', 'bulgaria', 'again', '!']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['i', 'can', \"'\", 't', 'wait', 'to', 'visit', 'bulgaria', 'again', '!']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.tokenize(\"I can't wait to visit Bulgaria again!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "CVz4kjcXHnnM",
    "outputId": "c1dbc46e-511e-4911-c6d1-60a611d70c29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1045, 2064, 1005, 1056, 3524, 2000, 3942, 8063, 2153, 999]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1045, 2064, 1005, 1056, 3524, 2000, 3942, 8063, 2153, 999]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = tokenizer.tokenize(\"I can't wait to visit Bulgaria again!\")\n",
    "tokenizer.convert_tokens_to_ids(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xnGGC53P9nC8"
   },
   "outputs": [],
   "source": [
    "def create_model(max_seq_len, bert_ckpt_file):\n",
    "\n",
    "  with tf.io.gfile.GFile(bert_config_file, \"r\") as reader:\n",
    "      bc = StockBertConfig.from_json_string(reader.read())\n",
    "      bert_params = map_stock_config_to_params(bc)\n",
    "      bert_params.adapter_size = None\n",
    "      bert = BertModelLayer.from_params(bert_params, name=\"bert\")\n",
    "        \n",
    "  input_ids = keras.layers.Input(shape=(max_seq_len, ), dtype='int32', name=\"input_ids\")\n",
    "  bert_output = bert(input_ids)\n",
    "\n",
    "  print(\"bert shape\", bert_output.shape)\n",
    "\n",
    "  cls_out = keras.layers.Lambda(lambda seq: seq[:, 0, :])(bert_output)\n",
    "  cls_out = keras.layers.Dropout(0.5)(cls_out)\n",
    "  logits = keras.layers.Dense(units=768, activation=\"tanh\")(cls_out)\n",
    "  logits = keras.layers.Dropout(0.5)(logits)\n",
    "  logits = keras.layers.Dense(units=len(classes), activation=\"softmax\")(logits)\n",
    "\n",
    "  model = keras.Model(inputs=input_ids, outputs=logits)\n",
    "  model.build(input_shape=(None, max_seq_len))\n",
    "\n",
    "  load_stock_weights(bert, bert_ckpt_file)\n",
    "        \n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "67GbMH3WRvZg"
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "kXyaQY4E9S10",
    "outputId": "232243e4-4332-483b-e3ac-1b2d259f1562"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 3, 1, 5, 2]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[4, 3, 1, 5, 2]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "classes = train['star_rating'].unique().tolist()\n",
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100it [00:00, 1933.25it/s]\n",
      "0it [00:00, ?it/s]\n",
      "100it [00:00, 1944.92it/s]\n",
      "100it [00:00, 1944.92it/s]\n"
     ]
    }
   ],
   "source": [
    "features = ClassificationData(train, test, tokenizer, classes, max_seq_len=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0kZxXxEcdRnI",
    "outputId": "a69a5f7a-3c5a-4442-af1f-6c8d8b93a947"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 128)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(100, 128)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "pilJksXUcsbq",
    "outputId": "695df877-c005-4418-8d7b-2bc1bf1f6f37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 101, 2061, 2521, 2061, 2204,  102,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 101, 2061, 2521, 2061, 2204,  102,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.train_x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "7LN7IoKJc1E4",
    "outputId": "1e7350e5-ecc7-4344-eb10-91e3a6fb055a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.train_y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QeEKod_Se4X4",
    "outputId": "f862156c-2d8e-4882-cb0d-5ce91db56fdf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.max_seq_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "colab_type": "code",
    "id": "TizDFiPC9n4b",
    "outputId": "e2c3b6a2-1656-4b59-aa7a-5ad6fef2426a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bert shape (None, 128, 768)\n",
      "bert shape (None, 128, 768)\n",
      "Done loading 196 BERT weights from: ./model/uncased_L-12_H-768_A-12/bert_model.ckpt into <bert.model.BertModelLayer object at 0x7fe320c5f240> (prefix:bert). Count of weights not found in the checkpoint was: [0]. Count of weights with mismatched shape: [0]\n",
      "Unused weights from checkpoint: \n",
      "\tbert/embeddings/token_type_embeddings\n",
      "\tbert/pooler/dense/bias\n",
      "\tbert/pooler/dense/kernel\n",
      "\tcls/predictions/output_bias\n",
      "\tcls/predictions/transform/LayerNorm/beta\n",
      "\tcls/predictions/transform/LayerNorm/gamma\n",
      "\tcls/predictions/transform/dense/bias\n",
      "\tcls/predictions/transform/dense/kernel\n",
      "\tcls/seq_relationship/output_bias\n",
      "\tcls/seq_relationship/output_weights\n",
      "Done loading 196 BERT weights from: ./model/uncased_L-12_H-768_A-12/bert_model.ckpt into <bert.model.BertModelLayer object at 0x7fe320c5f240> (prefix:bert). Count of weights not found in the checkpoint was: [0]. Count of weights with mismatched shape: [0]\n",
      "Unused weights from checkpoint: \n",
      "\tbert/embeddings/token_type_embeddings\n",
      "\tbert/pooler/dense/bias\n",
      "\tbert/pooler/dense/kernel\n",
      "\tcls/predictions/output_bias\n",
      "\tcls/predictions/transform/LayerNorm/beta\n",
      "\tcls/predictions/transform/LayerNorm/gamma\n",
      "\tcls/predictions/transform/dense/bias\n",
      "\tcls/predictions/transform/dense/kernel\n",
      "\tcls/seq_relationship/output_bias\n",
      "\tcls/seq_relationship/output_weights\n"
     ]
    }
   ],
   "source": [
    "model = create_model(features.max_seq_len, bert_ckpt_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "XjUkMjiGbC7f",
    "outputId": "2d7a63f4-4a28-4899-f1ac-31a0ef635e93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_ids (InputLayer)       [(None, 128)]             0         \n",
      "_________________________________________________________________\n",
      "bert (BertModelLayer)        (None, 128, 768)          108890112 \n",
      "_________________________________________________________________\n",
      "lambda (Lambda)              (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 768)               590592    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 5)                 3845      \n",
      "=================================================================\n",
      "Total params: 109,484,549\n",
      "Trainable params: 109,484,549\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_ids (InputLayer)       [(None, 128)]             0         \n",
      "_________________________________________________________________\n",
      "bert (BertModelLayer)        (None, 128, 768)          108890112 \n",
      "_________________________________________________________________\n",
      "lambda (Lambda)              (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 768)               590592    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 5)                 3845      \n",
      "=================================================================\n",
      "Total params: 109,484,549\n",
      "Trainable params: 109,484,549\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gYEHNOvbU9o8"
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "  optimizer=keras.optimizers.Adam(1e-5),\n",
    "  loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "  metrics=[keras.metrics.SparseCategoricalAccuracy(name=\"acc\")]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "colab_type": "code",
    "id": "oIjlbG8u9xis",
    "outputId": "7995d982-89ba-4b8f-f009-27391e02e49d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 22s 2s/step - loss: 1.5680 - acc: 0.2556 - val_loss: 1.4677 - val_acc: 0.4000\n",
      "12/12 [==============================] - 22s 2s/step - loss: 1.5680 - acc: 0.2556 - val_loss: 1.4677 - val_acc: 0.4000\n"
     ]
    }
   ],
   "source": [
    "log_dir = \"log/classification/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%s\")\n",
    "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_dir)\n",
    "\n",
    "history = model.fit(\n",
    "  x=features.train_x, \n",
    "  y=features.train_y,\n",
    "  validation_split=0.1,\n",
    "  batch_size=8,\n",
    "  shuffle=False,\n",
    "  epochs=1,\n",
    "  callbacks=[tensorboard_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6s31ZIADSGNp"
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DgE4VvMcI6Db"
   },
   "outputs": [],
   "source": [
    "#%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Pu0m0CWXJBq8"
   },
   "outputs": [],
   "source": [
    "#%tensorboard --logdir log/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5XLlcuVC1lLE"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 400
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure().gca()\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "ax.plot(history.history['loss'])\n",
    "ax.plot(history.history['val_loss'])\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'test'])\n",
    "plt.title('Loss over training epochs')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yZ7CVW_71m0Q"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 400
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure().gca()\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "ax.plot(history.history['acc'])\n",
    "ax.plot(history.history['val_acc'])\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'test'])\n",
    "plt.title('Accuracy over training epochs')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RGAECSj4AiD1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 4s 1s/step - loss: 1.4525 - acc: 0.4400\n",
      "4/4 [==============================] - 4s 1s/step - loss: 1.4525 - acc: 0.4400\n",
      "4/4 [==============================] - 5s 1s/step - loss: 1.4525 - acc: 0.4400\n",
      "train acc 0.4399999976158142\n",
      "test acc 0.4399999976158142\n",
      "4/4 [==============================] - 5s 1s/step - loss: 1.4525 - acc: 0.4400\n",
      "train acc 0.4399999976158142\n",
      "test acc 0.4399999976158142\n"
     ]
    }
   ],
   "source": [
    "_, train_acc = model.evaluate(features.train_x, features.train_y)\n",
    "_, test_acc = model.evaluate(features.test_x, features.test_y)\n",
    "\n",
    "print(\"train acc\", train_acc)\n",
    "print(\"test acc\", test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Leg5atNmPzGb"
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(features.test_x).argmax(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SZYriKx6UUOb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.06      0.11        16\n",
      "           1       0.00      0.00      0.00         6\n",
      "           2       0.00      0.00      0.00        29\n",
      "           3       0.44      1.00      0.61        43\n",
      "           4       0.00      0.00      0.00         6\n",
      "\n",
      "   micro avg       0.44      0.44      0.44       100\n",
      "   macro avg       0.19      0.21      0.14       100\n",
      "weighted avg       0.27      0.44      0.28       100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.06      0.11        16\n",
      "           1       0.00      0.00      0.00         6\n",
      "           2       0.00      0.00      0.00        29\n",
      "           3       0.44      1.00      0.61        43\n",
      "           4       0.00      0.00      0.00         6\n",
      "\n",
      "   micro avg       0.44      0.44      0.44       100\n",
      "   macro avg       0.19      0.21      0.14       100\n",
      "weighted avg       0.27      0.44      0.28       100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(features.test_y, y_pred)) #, target_names=classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lS-5TbGDP4rr"
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(features.test_y, y_pred)\n",
    "df_cm = pd.DataFrame(cm, index=classes, columns=classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DgkPl9gLS4DL"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 270,
       "width": 363
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fhxAOyh3bTAihBXA3cCzZBWWOizFOTyMeK9eSJEkqF7fk9gfkts2EEKqAO4DTgLXACTHGf6YVgJVrSZIk1SmNnuvGzK/3925kF4fZ1I3Al8n2aJ8aY5yQZgAm15IkSapTYlPxbaJ/vb+Xb3owhPB94GtADXBGjPGhtAOwLUSSJElFL4TQMtfSsSXfzO2rgWc3ef23gYuBDPDlGONv0o/SyrUkSZLqK962kJ2BB0IItwETYoxvQe3NifuSTaxH5869Jcb4wYYXhhDGAlflHp4XY7wzX0GaXEuSJKlUDALuBAghrCbb+tEZaFvvnLuBizZ53Q25fQ3w7VwVuzGDY4wNLVCzTUyuJUmSVKd4p+J7m+zy5kcCBwO9gR5kV2WcSbYN5K4Y48QGXruhnaQF0Gsrn9MySZAm15IkSapTpG0hMca1wO9y24d97dZ6tVNTVjc0hhAOCiGMCyE8FkKYEUJYEkJYE0KYH0IYH0I4vtAxFps+fXrz85/9iHlzJrNi2SxmTHuOH13/Xbp161ro0LQJx6q0OF6lZfBhg7juzqt57JWHmDj7bzz68oPc/JvrGXrEkEKHpnq8rlQKqjKZ4vx10hQhhJ8AX6n31HKy1fl29Z57APhsjHFdks9q1aZPyf+DGzCgH888NZ5evXoy/pHHiHEGgw86gJEjD2VqnMGw4cezaNEHW38j5Z1jVVoqYbwO2H63QoeQmvMu/yqfP3s0C99+l0n/eI7Fi5bQvUc39tw38MIzL3HL1T8pdIiJTHlvZqFDSEUlXFfVa+c3W3V1S5ZfelJecpxO1z5QFN8v38qtLeRZYCrwNDAtxrgcIISwM3Ae2btITwIuAcYVKshicevN19CrV0/OH3s5t91+V+3z1193BWPHfplxV13MOedeUsAItYFjVVocr9Jx/Ohj+fzZo/nT/X/hexf9kOp11Rsdb9kqUeulUuR1pVJRVpXrrQkh/Bo4HZgVY0xUdin1yvWAAf2YNnUSs2fPY489h1L/vwedOnXkrXlTqKqqoneffVm5clUBI5VjVVoqZbzKoXLduk1r/vTSH1izeg0nHjp6s8S6XJRD5bpSrquiqVxffGJ+Ktc/eLAovl++lVXP9TZ4MbffqaBRFIERw4cC8PjfnmbTH1jLl69g0qQX6dixA0MOObAQ4akex6q0OF6l45BhB7Hd9t154tGnydRkOPTIIXz+nNGc+qWT2efAvQsdnurxumpmNZn8bBWi0pLrobn97IJGUQTCHtmq0/Tpsxo8Pn1G9h/RwIEDmi0mNcyxKi2OV+n4yP57ArB2zVrumXAnN/76Os677Kt8Y9zX+MUf7+CnD9xMt+28Ua4YeF2plJR9ch1C6BRC2De3ms8puadvLWRMxaBL184ALFmytMHjS5cuA6Bbty7NFpMa5liVFserdHTv0R2A0886lUwmw5nHn8Ow3Y/m1CPG8OyTLzDoY/vz/Z9dtZV3UXPwumpmmZr8bBWi3G5oBCCE0BdoaGWd1cD3Yoy3N3NIkqQi06JFtr60vno93xhzKQveegeAmVNn8c0vXcYDz9zLgUMPYJ8D9+Y/k18rZKiSSki5Vq7XAwtz29rcc9XAtcBthQqqmCxdkv2V37Vrw7/yu3TJVgkWL264SqDm41iVFserdCxbuhyA+Or02sR6gzWr1vDsky8AsPf+ezV7bNqY11Uzs+c6kbJMrmOMC2KMO8YYdwTaAwH4FfBd4JUQQsXfqRKnZe8eb6w/beDu/YHG+9vUfByr0uJ4lY65M+cBsDyXZG9qWS6ha9u+bbPFpIZ5XamUlGVbSH0xxhpgGvClEMJi4OvAr0MIB+WOVaQnn5oEwMePGkZVVdVm0xoNHTqYFStW8tzzkwsVonIcq9LieJWOF5+ZTE1NDf332HWzsQLYLWQTtrfnLShEeKrH66p5ZSqoypwPZVm53oJbcvsDclvFmjVrLhMmPEn//rtw9lljNjp25XcupFOnjtxz7wMlPV9ouXCsSovjVTremb+QZx6fRO++O3LqmSdvdOyQ4YMZMuJgli5exqQnni9QhNrA66qZ2RaSSKUtItOauh7sU2KMv2vqe5X6IjKw+VKyU6dO5+DBgxg58lDitJkcPuwzJb+UbLlwrEpLJYxXOSwiA7BD757c+cjt7NinFy888xLx1enstHNvhh9zGGTgW2d9lycefarQYSZSDovIQGVcV8WyiMyyrx2blxyn881/Korvl2+VllzvAcTcw0/FGB9t6nuVQ3IN0LfvTlx5xYUcPWoEPXp0Z8GCd3l4/GOMu/oGFi9eUujwVI9jVVrKfbzKJbkG6LZdV878+hiGjTqU7XfowYrlK5jy/L+5+5Z7ef2VNwodXmLlklxD+V9XRZNcn/vJ/CTXtz5aFN8v38omuQ4htARqYoyNfqEQws+BM8nOHLJDjLHJP3HLJbmWpKYop+S63JVTcl3uTK7LQznd0Lgz8EBusZgJMca3AEIILYB9gW8Co3Pn3pIksZYkSSpbFdQfnQ/llFwDDALuBAghrAaWA52B+vMo3Q1c1OyRSZIklQKT60TKKbl+m+zy5kcCBwO9gR5kV2WcCTwL3BVjnFiwCCVJklTWyia5jjGuBX6X2yRJktQE5XI/XqFU2jzXkiRJUt6UTeVakiRJKbDnOhGTa0mSJNUxuU7EthBJkiQpJVauJUmSVCtj5ToRK9eSJElSSqxcS5IkqY6V60SsXEuSJEkpsXItSZKkOjWFDqC0mVxLkiSpljc0JmNbiCRJkpQSK9eSJEmqY+U6ESvXkiRJUkqsXEuSJKmONzQm0qTkOoTwSIoxZGKMn0nx/SRJktRE3tCYTFMr18cCGaAqhRgcQUmSJJWFpibXD2JSLEmSVH5sC0mkScl1jPHktAORJEmSSp03NEqSJKmWPdfJmFxLkiSpjm0hieQtuQ4h9AcOAnoC7WKMN+TrsyRJkqRikHpyHUL4GPAj4JBNDt1Q75wAPAGsAfaNMS5LOw5JkiR9eBkr14mkukJjCOE04ClgCNlp+lbTwHR9McYIzAR2AU5MMwZJkiSpUFJLrkMIA4FfkK2Gjwf2Bnpt4SV/IJt4H51WDJIkSUqoJk9bhUizLeQbQGtgQozxBIAQQsctnP9cbr9/ijFIkiRJBZNmcn0k2YVlrtnG8+fk9n1TjEGSJEkJ2HOdTJrJdZ/c/j/beP7q3L5dijFIkiQpCZPrRNK8oXFtbt9mG8/vmdsvTjEGSZIkqWDSTK7n5vbb2kM9PLePKcYgSZKkBDI1+dkqRZrJ9eNkZ//42tZODCG0Ay4k26P9WIoxSJIkSQWTZnJ9E9lFYY4JIfwihNC2oZNCCDsBjwABWA7ckWIMkiRJSsDKdTKp3dAYY3wzhHAO8H/AGcAJwKQNx0MIPwd2A4aSnbIP4MwY46K0YpAkSVIylZQI50OqKzTGGH8BjAaWAF2BT5Bt/QD4IjCC7A2PS4H/iTH+Ps3PlyRJkgopzan4AIgx/jaE8Gey1eujyLZ/dCXbAjILmADcGWNckvZnN6fWLVP/R6c8WLe+utAhSGXp8eEtCx2CttH2DxQ6ApWcTFWhIyhpeckQY4zLgFtzmyRJklQRLL9KkiSplj3XyeQ9uQ4hVAGdgOUxxszWzpckSVLhZGpsC0kiL8l1COHjwBeAQ8kui14FZEII84GJwF0xxsfz8dmSJElSoaSaXIcQugH3Asfknqr/06cK2Bk4BTglhPAX4PQYo8ufS5IkFQnbQpJJLbkOIbQB/k52+fMq4H2yqy9OJTtTSCdgT7KJdw+y0/Q9HkIYGmNcl1YckiRJUqGkWbm+ADiA7LzW3we+G2Ncs+lJuZUbrwAuAQblXnddinFIkiSpiTJOxZdImovIjCabWN8RY/xWQ4k1QIxxTYzxW8BPyFa4T0sxBkmSJKlg0kyud8/tf7KN59+2yeskSZJUYJma/GyVIs22kJVAO2D+Np7/dr3XSZIkqQgU61R8IYRdgBOBI4H9gF7AWrIrgP8FuCnGuOBDvN/DwGdyD38ZYxyTRpxpVq5fze0HbuP5G857dYtnSZIkqaKFEHYG5gA/Bo4lOwPdaqA9sC9wMfBaCGHkNr7f8dQl1qlKM7m+g2wP9SXbeP6luf22tpFIkiQpzzKZ/GwJtczt/wz8P2C7GGNXoAPwSWA20B14OISw45beKITQCbgZWEp2VrtUpZZcxxh/RzbQ40MIvwsh9G3ovBBC3xDC74BPAzfHGO9PKwZJkiSVpQ+AA2KMx8YY/xBj/AAgxrg2xvgXsgn2aqAL8JWtvNc4spXvbwML0w60ST3XIYSbt3B4NnAScEII4WU2n+d6ENmkfg7QIoRwU4zx/KbEIUmSpHQVY891jHEJ8K8tHJ8aQngOGAEc2Nh5IYRBwHnAK2Qn1zgx3UibfkPjuWSn3WtMFdny/WDgoE2e32BX4Jzc3ybXkiRJRaAYk+tt9H5u37KhgyGEFsBPyRZ5z44xrg8hpB5EU5Prl9lyci1JkiQ1ixBCK+DQ3MPGJss4l2zR984Y47P5iqVJyXWM8aCtnyVJkqRSk8LNh4VwDrAjUAP8ctODIYQ+wNVkq9sX5zOQNOe5liRJkppVCGFf4Nrcw1tjjK83cNrNQGfgf2OM7zdwPDUm15IkSapVSj3XIYTewMNk57ueTANV6RDCsWRvXHwOuDPfMZlcS5IkqVYmUxrJdQhhO2AC0B+YDnwqxrh6k3M6kp0VZD3Zmxjz3vSSl+Q6hNCG7DQoewBdt/Y5McYb8hGHJEmSyk8IoSvwV+CjwDzgqBhjQ3NWXwTsAvwcmJ5bQKa+DTOLtKp3bEWSJDzV5Do3xcm3gLFkV8nZVibXkiRJRSBTU+gItixXjX6U7Mwf75BNrOc1cnq/3P5/c1tjTsttkK2Ez2lqfGlXrn9LdgGZKmAl2SUpIbuqTiegde5xBlgBrEn58yVJklSmQgjtgT8CQ8nO/HFUjHF6YaPaWGrJdQjhJOBksktPng48RLa/JQPsHGNcGULYM3fsAmARcHKM8aW0YpAkSVIyNUXac51rO34QGAksBkbFGF/b0mtijGOAMVt4zyeB4cAvc+cm1iKNN8k5g2wi/dMY44MN9arEGKfGGC8nO8l3F+DPIYSdUoxBkiRJCWQyVXnZkgghtAR+AxwDLAM+EWN8OYWvm7o020I2LCxzXwPHNkriY4yvhBC+C/wYuBD4eopxSJIkqbwcSrb1GLJtxg9vYenyN2OMg5slqgakmVz3yO3n1nuumuxdmB2A5Zuc/zDZ5PqTmFxLkiQVhSKd57p+obZdbmvM6i0cy7s0k+u1DbzfEmA7YGfg3U2Orcrt+6YYgyRJkspMjPFJshNmpP2+I9J+zzR7rjdUrHvXey7m9oc1cP6BuX2RT/giSZJUOTKZ/GyVIs3k+rncvn+95x4n+yvjwhDChnkGCSH0Aa4jewPkKynGIEmSJBVMmm0hjwBfBI4iOw0fwE/JLiizEzA1hPAC2R7s/cmuAQ9wS4oxSJIkKYEi7bkuGWlWricAd5KdHgWAGOM7wCm559oCh5Od9LsD2Yr2NTHG36cYgyRJkhKoyVTlZasUqVWuY4yraWBZyRjjhBDC7sBoYD+ySfZs4Pcxxn+n9fmSJElSoaW9/HmDYozvATc3x2dJkiSp6ZIu+FLpmiW5bi4hhM5kl8QcTHZRm8HUzb+9V4xxaqFiK0YnnPBJDj/8EPbd9yPss89edOnSmfvue4gvfnFsoUNTA/r06c2VV1zI0aNG0KNHdxYseJfxj/yVcVffwOLFSwodnjbheBWPqk5daD34MFodMISWOw+gxXbbQ/U61s+bzdqnHmPtU3/ZfCqDVq1pM/JTtBk2iha9dqKqdRtq3n+X6v9MZvWff0fmvYWF+TIVzutKpaCskmvgSOpuptRWXHzxuey3394sW7ac+fPfoUuXzoUOSY0YMKAfzzw1nl69ejL+kceIcQaDDzqA8792JkcfPYJhw49n0aIPCh2mchyv4tJ6yHA6fOnr1HzwHtWvvcK69xdS1XU72gw+nA5f+Sat9j+YlTdeWfeCFi3odNmPaLXnPqyfP5d1k/5OZt06Wu22J22POZE2h49i2RXnUjN/bqOfqfR5XTWfSpo2Lx+alFyHEAalGUTKa8O/C7wEvAgJkABvAAAgAElEQVTMB36W4nuXlYsuGsf8+QuYOXMOhx8+hAkT7i90SGrErTdfQ69ePTl/7OXcdvtdtc9ff90VjB37ZcZddTHnnHtJASNUfY5XcalZ8BbLf/gtqqc8t1HWsPq3P6fz1XfQ5pDhrDt4GOteeBqA1oMPp9We+7DuP5NZce03N3pNu5PH0O6kM2h77Cms+ul1zf5dKpnXVfOppJsP86Eq04SfJyGEGrJzVKchE2NMpYIeQmgZY1xf7/GuZG+ehJTbQtq371dWv+s2JNfl1haybn11oUNIbMCAfkybOonZs+exx55DqX/NdurUkbfmTaGqqoreffZl5cpVW3gnNYdKGa/3Ttqj0CGkou1nTqP9qWey5q8Psuru7MywbY87lfajv8KqX93Gmr/8YaPzW+46kM7X/ox1kyex4vrLChHyh7b9A9MKHUJilXJdVa+dXxRZ7Sv9Pp2XHGf/uY8UxffLtyRT8VWluKWifmItlYsRw4cC8PjfnmbTH8PLl69g0qQX6dixA0MOObChl6uZOV4lJvcDPLO+7l8f69+aA0Cr/Q+Gqo3/FdVq0McAWPdqmv+Hq7bG66p5ZTJVedkqRVMrxvukGoWkRoU9dgNg+vRZDR6fPmM2o0aNYODAAfzjiX82Z2hqgONVQlq0oM3howCo/teLtU9XT3mOtc8/TZtDhtH5ul9Q/Z/JZKrX0XLAHrQK+7DmsQdZO8Hbe5qT15VKSZOS6xjja2kHIqlhXbpmbzRdsmRpg8eXLs2u29StW5dmi0mNc7xKR7vPfpmWuwxg3ZTnqP73ixsdW3njFdScdAZtT/gcbfvuWvv8uv9MZu3Ev0FNTTNHW9m8rpqXNzQmU26zhUiStFVtjj6Rdseewvr5c1l52zUbH2zdmg5nf4vW+x3MqrtuYt1L/ySzdg2t9vgo7c84j05X3MSKG79L9eSJhQleyjNvaEwmzeXPJeXB0iXZikzXrg1XZDZMobh4ccMVHTUvx6v4tRl1PB3GnMf6t+awfNwFZFYs2+h4u0+Pps2QEay6/07W/v2PZJZ8AKtWUv2vF1hx45VUtWpNhzPOLVD0lcnrSqXEyrVU5OK0mQAMHDigweMDd+8PNN6LqObleBW3tp84ifafP5f182ax/HvfILN08WbnbLhpsfr1KZsdq5k3k5rlS2nRc0eqOnUhs9xkrjl4XTWvSrr5MB9MrqUi9+RTkwD4+FHDqKqq2mwKqqFDB7NixUqee35yoUJUPY5X8dowxV71nOmsuOZCMssaToyrWrUGoEWXbmzWWd2qNVXtOgCQqV6Xx2hVn9eVSoltIVKRmzVrLhMmPEn//rtw9lljNjp25XcupFOnjtxz7wMlPbdrOXG8ilPbEz6XTaxnRVZc/Y1GE2uA6vif7Gs+cxrkEu0N2p18BlWtWlE94w1Y7Rg2F6+r5lWTqcrLVimatIhMqXARmS077rhRHHdcdhqqXr16MmrUCGbNmsvEiS8A8P77H3Dppd8rZIiJlcMiMrD5sr9Tp07n4MGDGDnyUOK0mRw+7DMu+1tEKmG8SmkRmdbDjqbjWZeQWb+eNX99EFau2Oycmv++w9qn/wpAVfft6TzuNlr02IH17y6g+l8vkFm7llbho7TafS8ya1az/HvfYP3015v7qzRJOSwiA5VxXRXLIjLP73RiXnKcQ95+sCi+X76ZXDdROSTXl102lssvv6DR43Pnvsmeex7WjBGlr1ySa4C+fXfiyisu5OhRI+jRozsLFrzLw+MfY9zVN7B48ZJCh6dNlPt4lVJy3e6kM2h38pgtnlP9+issH1f3v4dVnbvS9tOfpfUBQ2jRsze0qCLzwfuse20Ka/54HzVvv5nnqNNTLsk1lP91VSzJ9XN5Sq6HmFyXPpNrlVNyLRWTUkquK105JdflrliS60m9T8pLjjN0wQNF8f3yrexuaAwhbF/vYfd6f3fb5NiiGKOrAEiSJCk1ZZdcA/9t5PlnN3ncH5iT31AkSZJKi1PxJZOX5DqE0BIYBhwE9ATaxRi/tsk5bYBMjNG5jCRJklQWUk+uQwj/A/wQ6LvJoa/VO2d3YCpQHULYOcbYWLX5Q4sx+nNLkiSpieyZTSbVea5DCBcB9wE7A9XANGCzZDfGOAP4G9AaODnNGCRJktR0GaryslWK1JLrEMIg4Nrcw1vItoMM3sJLHiSbeB+VVgySJElSIaXZFnIB2WT5vhjj+QAhhI5bOP/F3H6fFGOQJElSAjVONpxImm0hw4AM8ONtPP+t3L53ijFIkiRJBZNm5bpXbj9jG8/fMEtI6xRjkCRJUgI1FdQfnQ9pJtcrgG5AB2DxNpy/oWL9QYoxSJIkKYFKuvkwH9JsC5mZ2x+8jeePyu1fTTEGSZIkqWDSTK7/QvaGxm+GELb4kyeEsB1wIdke7T+mGIMkSZISqMnTVinSTK5vBpYAQ4AJIYSdGjophDAY+AfQB3gX+L8UY5AkSZIKJrWe6xjj+yGE04CHgSOAeWRXYQQghPA4sBvQj2yFey1waoxxZVoxSJIkKRl7rpNJdYXGGOOjwEhgVu69P5I7VAUcCeya+3smMDLG+FSany9JkiQVUpqzhQAQY5wYQtgD+BTZ1RcD0BVYTjbpngCMjzGuT/uzJUmSlEwl9UfnQ+rJNUCMMQP8KbdJkiSpRJhcJ5NqW4gkSZJUyfJSuZYkSVJp8obGZFJLrkMIJzb1tTHGB9OKQ5IkSSqUNCvXfyC7KMyHlUk5DkmSJDVRjYXrRNJOapsyHA6hJElSkagxNUskzeS68zZ81k7AJ4CLya7meBLZ6fkkSZKkkpfmCo0rtuG0JcAbIYTfAc8CDwGD0opBkiRJyTSlx1d1CjIVX4zxLeA7wADgokLEIEmSJKWtkPNcP57bn1zAGCRJklRPTZ62SlHIWTo2tJHsXMAYJEmSVE9NlTc0JlHIyvUhuf2qAsYgSZIkpaYglesQwu7AzWR75l8sRAySJEnanDc0JpPmCo03b8Np7YEADMl9dg1wfVoxSJIkSYWUZuX6XLbtx86GRp5VwNgY499TjEGSJEkJVNLNh/mQZnL9MltOrjPAamAB8DxwX4xxQYqfL0mSJBVUmovIHJTWe0mSJKkwapwsJJFCTsUnSZKkIlOD2XUSad7Q+CbZNp2zY4x/Tut9JUmSpFKRZuW6J9AamJLie0qSJKkZORVfMmkuIvNObr8uxfeUJEmSSkaalesngc8Bg4FHU3zforRufXWhQ5Ckgul4y52FDkHb6oHDCx2BSow3NCaTZuX6RrJV66tCCO1TfF9JkiQ1k5o8bZUiteQ6xvgKMAbYC5gYQjgmhOBsJJIkSaoYac4W8u/cn6uA/YA/A2tDCPOBlVt4aSbGuF9acUiSJKnpvKExmTQryx9t4Lm2wICtvM4xlCRJ0haFEDoDI8ne33dQbt8jd3ivGOPUbXiP9sBXgJOBAHQGFgKvA3+LMf4oaZxpJteJg5EkSVJhFfENjUcCDzX1xSGEjwB/pK7wu45sd8Uuue3jpJDPNim5DiHsAhBjnLfhuRjjN5MGI0mSpMIq8psP3wVeAl4E5gM/25YXhRB2Bp4AdgAmApcCE2OMNSGEjsAg4MQ0Amxq5XoOUBNC6BJj3FI/tSRJkpSGP8YYH97wIISw64d47U/IJtZPAkfHGNduOBBjXAE8k9sSS9IWUrz/p4EkSZKapFgr1zHG9U15XQhhP+CTuYdn10+s8yHNea4lSZKkYnNabv9KjPGNfH+Y81BLkiSpVqb8ehM+lttPCSF0Ay4HTgJ2Aj4AJgE3xBj/mcaHWbmWJElSORtY7++XgG8AfcjOFNILOAF4OoTwjTQ+zORakiRJtcpw+fNuuf0ZZKfcOwfoEmPsDvQnu/BhFfDDEMLwpB+WtC3ktBDCmqRBxBh/lfQ9JEmSlFyx3tCYQIt6+2tijLdvOBBjnBNCOBmYBuwMXAw8leTDkibXP0n4esiu0GhyLUmSpHxYDnTP/X3TpgdjjKtDCHcA1wAjQggtmzozCSRvC6lKaZMkSVIRyORpK6C3c/tFMcb3Gjkn5vbtqVtSvUmSVq4/CaxK+B6SJElSvrwK7P0hzk/0WyBpcv20KzRKkiSVj5ry6yn4G3AKsF0IYftGqtd75vbLgPeTfJizhUiSJKlWGc4W8hDZvmuAsZseDCG0A76ae/jXGGOicF1ERpIkSSUhhLB9vYfd6/3dbZNjizYkyTHG90MI1wLfAy4KIbwN/CJ3I2M/4FayM4WsBa5OGqPJtSRJkmoV+VR8/23k+Wc3edwfmFPv8bXAXsDpwG3AjSGE+rOIrAXOiDH+K2mAtoVIkiSprMUYMzHGzwGnAv8g21vdEZgH3AUcEGP8bRqfZeVakiRJtQo8bd4WxRgT3W4ZY7wfuD+lcBrU1OS6P4AzhUiSJJWXMpwtpFk1KbmOMc5NOxBJkiSp1NkWIkmSpFpFfkNj0fOGRkmSJCklVq4lSZJUq5hvaCwFVq4lSZKklFi5liRJUq0aa9eJmFxLkiSpljc0JmNbiCRJkpQSK9eSJEmqZVNIMlauJUmSpJRYuZYkSVIte66TKavkOoQwBrhrK6etiDF2aoZwSkKfPr258ooLOXrUCHr06M6CBe8y/pG/Mu7qG1i8eEmhw1M9jlVpcbyK2x//+g8uveqHAFx58fmc/Oljao9Nmzmbe343ntfjdBb+9z2Wr1jJdt270X+XvpxywrEcNXwoVVVVhQq9onldNY8a/+udSFkl1/WsAxY1cmxFcwZSzAYM6MczT42nV6+ejH/kMWKcweCDDuD8r53J0UePYNjw41m06INChykcq1LjeBW3BQv/yzU33E6H9u1ZuWrVZsdfnzqDfzzzLPvuvSf77/MROnXsyHuLFvHUxOe54LKrOe6YI7n22xcWIPLK5nWlUlGuyfWkGOOIQgdR7G69+Rp69erJ+WMv57bb6wr+1193BWPHfplxV13MOedeUsAItYFjVVocr+KVyWT49jU30LVLZ44afih33/fAZud88uPDOf5TH9/s+eUrVjD6yxfwx8f+zuiTjmOfj4TmCFk5XlfNx3muk/GGxgo1YEA/Ro0awezZ87j9jrs3OnblVdezfPkKTj/tJDp0aF+YAFXLsSotjldxu+f343l+8r+4+rKv0759uwbPadOmTYPPd+rYkUMPPhCAuW/Oz1uM2pzXlUqJyXWFGjF8KACP/+1pMpmNf6EuX76CSZNepGPHDgw55MBChKd6HKvS4ngVr5lz5nHjHXdx+v/7DAftv8+Hfv2q1at5/uV/ATBwt/5ph6ct8LpqXpk8bZXC5LpChT12A2D69FkNHp8+YzYAAwcOaLaY1DDHqrQ4XsWpuno9l151Pb179eT8r47ZptfMe+ttbrvzHm752a+48gc38alTzmTajNmc+blTCLubXDcnr6vmVZOnrVKUa8/13iGE14ABQDUwF3gcuDnGOLugkRWJLl07A7BkydIGjy9dugyAbt26NFtMaphjVVocr+L0k7vuZer0mfzqjutp17btNr1m3ltvc8cv7q193Lp1K75xzpcY89mT8hWmGuF1pVJSrsn19kAP4AOgC7B3bvtKCOHMGONvChmcJKn5/Pu1qfz81/dzxqknsv9H99rm1x025CBenfgX1lVXs+Cdd/nzhCe46ae/5KVX/sON37uc1q1b5zFqqXC8oTGZcmsLeRu4Avgo0C7G2APoBHwKeB1oD/wyhDCscCEWh6VLsr/yu3Zt+Fd+ly7ZKsHixQ1XCdR8HKvS4ngVl+rq9Xxr3PX027kP5/3v55r0Hq1btWKXvjtx1hdP49wzP8dTE1/gnt+PTzlSbYnXlUpJWVWuY4wTgAmbPLcGeDSEMBF4Cdgd+D4wtPkjLB5x2kyg8f60gbl+wsb629R8HKvS4ngVl5WrVjEnN7PHoJGfafCcK39wE1f+4CZO/3+f4ZKxX93i+x025CBu/MldvDjlP3xh9Mmpx6uGeV01L+vWyZRVcr0lMcYlIYRrgF8AQ0II28cY3yt0XIXy5FOTAPj4UcOoqqra6O7rTp06MnToYFasWMlzz08uVIjKcaxKi+NVXNq0ac2Jxx7d4LE3ps3gjWkzGbTv3uy6S1/224aWkXf/m/3XRquWLVONU1vmdaVSUm5tIVvzfG5fBVT0rd6zZs1lwoQn6d9/F84+a8xGx678zoV06tSRe+59gJUrN1+9TM3LsSotjldxade2LVddOrbBbcRhQwD49CeO4qpLx/KJo4YD8Oob0xp8r0UfLObHP8kuXjJs6ODm+QICvK6am7OFJFMxlWtt7tyvfYtnnhrPTTdezRFHHMbUqdM5ePAgRo48lDhtJt/+zg8KHaJyHKvS4niVtiu+fxOLly5ln70CvXv1pEXLFry9YCHPPPsSq9es4YhhH+OET40qdJgVx+uq+XhDYzKVllwfUu/vOYUKoljMmjWXQz72Sa684kKOHjWCTxxzBAsWvMtNN/8f466+gcWLlxQ6ROU4VqXF8SptY0afxD+ensQb02Yy8YXJrFtXTfeuXTj4wP047ugjOObIbGuCmpfXlUpF1aYrHZWqEEJVjLHRLxNC6AK8COwBvBBjPKSxc7dFqzZ9yuMfnCQ1waq3nyl0CNpG7Xc6vNAhaBtVr51fFL/aLtj11LzkOD+e89ui+H75Vk6V634hhN8CPwcejzHOAwghtAGOAH5INrGuAS4tWJSSJEkqW+WUXEO27eMQgBDCamAF2UVkNsz0vxL4aozxH4UJT5IkqbhV0s2H+VBOyfVC4GvAYcB+QE+gK9kEezrwd+COGOPcgkUoSZJU5DLe0JhI2STXMcZVwC25TZIkSWp2ZZNcS5IkKTnbQpKptEVkJEmSpLyxci1JkqRaLiKTjMm1JEmSaplaJ2NbiCRJkpQSK9eSJEmqZVtIMlauJUmSpJRYuZYkSVItp+JLxsq1JEmSlBIr15IkSarl8ufJmFxLkiSplm0hydgWIkmSJKXEyrUkSZJq2RaSjJVrSZIkKSVWriVJklTLnutkTK4lSZJUqyZjW0gStoVIkiRJKbFyLUmSpFrWrZOxci1JkiSlxMq1JEmSatVYu07E5FqSJEm1nOc6GdtCJEmSpJRYuZYkSVIt57lOxsq1JEmSlBIr15IkSarlDY3JmFxLkiSpljc0JmNbiCRJkpQSK9eSJEmq5Q2NyVi5liRJklJi5VqSJEm1Mhl7rpOwci1JkiSlxMq1JEmSahXzVHwhhBbAGcBpwH5AN2AFEIFHgJtjjMsKF6GVa0mSJNVTk6ctqRBCB+Bx4BfAkcD2ZBPrLsDBwNXAf0IIA1L4uCazci1J+tCG7jum0CFIqjzfBo4AMsC3gDtijEtCCG2AE4E7gH7A/+XOKwiTa0mSJNUq4kVkRuf2d8UYv7/hyRjjWuC3IYR2wF3AyBBC9xjjB4UI0rYQSZIklYJeuf2URo5Prvd3hzzH0igr15IkSapVxDc0zgECcEAjxw/M7RfGGOc3S0QNMLmWJElSrSKe5/rnwPXAF0II09m45/oE4Mdk+7EvLGCMtoVIkiSpJNwI3AZUAdcCi0MIi4FVwG+BqcCnY4z3FC5Ek2tJkiTVU6xT8cUY1wNjgW8A1bmnu1KXz3YGeqbwUYmYXEuSJKnohRB2BCYCPwLuJbuITCdgIHApMAD4RQjh2oIFiT3XkiRJqqeIp+L7FdnFYu6MMZ5Z7/kZwPdDCPNz51wUQrgnxvhaIYK0ci1JkqRaNWTysiURQvgI8PHcwx83dE6M8dfA+2Tz2+MSfWACJteSJEkqdnvV+3v2Fs6bldvvmr9Qtsy2EEmSJNUq0qn46t8TuQvZmUEa0i+3X5bfcBpn5VqSJEnF7l/1/v7fhk4IIRwH7JB7+HzeI2qEybUkSZJqFWPPdYxxFjAh93BsCOHaEMIOACGETiGEMcDdueNzgEcSfWACJteSJEkqBWOAN8jmr5cAC0MIS8m2gNwFbAcsBE6MMa4tVJAm15IkSaqVydN/kooxLgAOJLuQzNPAIqADsBR4GRgH7BNjnJL4wxLwhkZJkiTVqinOGxoBiDGuAm7KbUXJyrUkSZKUEivXkiRJqlW8devSYOVakiRJSomVa0mSJNVKOm1epTO5liRJUi2T62RsC5EkSZJSYuVakiRJtTJFPBVfKbByLUmSJKXEyrUkSZJq2XOdjMm1JEmSaqWxVHklsy1EkiRJSomVa0mSJNXyhsZkrFxLkiRJKbFyLUmSpFre0JiMlWtJkiQpJVauJUmSVMue62RMriVJklTLtpBkbAuRJEmSUlJWyXUIYZcQwtgQwh9DCPNCCGtCCMtCCP8KIXw/hNC70DEWmz59evPzn/2IeXMms2LZLGZMe44fXf9dunXrWujQtAnHqrQ4XqVl8GGDuO7Oq3nslYeYOPtvPPryg9z8m+sZesSQQoemeryumkcmT/+pFFXl0lcTQtgZmAtU1Xt6KdARaJl7/AFwUozxiaSf16pNn5L/BzdgQD+eeWo8vXr1ZPwjjxHjDAYfdAAjRx7K1DiDYcOPZ9GiDwodpnCsSk0ljNcB2+9W6BBSc97lX+XzZ49m4dvvMukfz7F40RK69+jGnvsGXnjmJW65+ieFDjGRKe/NLHQIqaiE66p67fyqrZ+Vf/vu+LG85Dj/fufZovh++VZOPdcbEug/A3cDf48xfhBCaAMcCdwG9AceDiGEGOM7hQmzeNx68zX06tWT88dezm2331X7/PXXXcHYsV9m3FUXc865lxQwQm3gWJUWx6t0HD/6WD5/9mj+dP9f+N5FP6R6XfVGx1u2atnIK9XcvK6aT02ZFF4LpZwq112BXWOM/2rk+J7AFKAdcGWM8btJPq/UK9cDBvRj2tRJzJ49jz32HLrRncGdOnXkrXlTqKqqoneffVm5clUBI5VjVVoqZbzKoXLduk1r/vTSH1izeg0nHjp6s8S6XJRD5bpSrqtiqVzv3euQvOQ4ry18vii+X76VTc91jHFJY4l17vhU4LncwwObJ6riNWL4UAAe/9vTm025s3z5CiZNepGOHTsw5JCK/0dVcI5VaXG8Sschww5iu+2788SjT5OpyXDokUP4/DmjOfVLJ7PPgXsXOjzV43WlUlI2yfU2ej+3r/j/ny/ska06TZ8+q8Hj02fMBmDgwAHNFpMa5liVFserdHxk/z0BWLtmLfdMuJMbf30d5132Vb4x7mv84o938NMHbqbbdt4oVwy8rppXTSaTl61SVExyHUJoBRyae/hqIWMpBl26dgZgyZKlDR5funQZAN26dWm2mNQwx6q0OF6lo3uP7gCcftapZDIZzjz+HIbtfjSnHjGGZ598gUEf25/v/+yqAkcp8LpSaamY5Bo4B9gRqAF+WeBYJEkF1qJF9l+B66vX840xl/KvF/7DqpWrmDl1Ft/80mUsfPtdDhx6gC0iqjhOxZdMRSTXIYR9gWtzD2+NMb5eyHiKwdIl2V/5Xbs2/Cu/S5dslWDx4oarBGo+jlVpcbxKx7KlywGIr05nwVsbTyC1ZtUann3yBQD23n+vZo9NG/O6al62hSRT9sl1buGYh4H2wGTg4sJGVBzitOzd4431pw3cvT/QeH+bmo9jVVocr9Ixd+Y8AJbnkuxNLcsldG3bt222mNQwryuVknKa53ozIYTtgAlk57eeDnwqxri6sFEVhyefmgTAx48aRlVV1WbTGg0dOpgVK1by3POTCxWichyr0uJ4lY4Xn5lMTU0N/ffYdbOxAtgtZBO2t+ctKER4qsfrqnlVUgtHPpRt5To37/VfgY8C84CjYowLCxtV8Zg1ay4TJjxJ//67cPZZYzY6duV3LqRTp47cc+8DJT1faLlwrEqL41U63pm/kGcen0Tvvjty6pknb3TskOGDGTLiYJYuXsakJ54vUITawOtKpaRsFpGpL4TQkWzFeijwDjAsxjg9zc8o9UVkYPOlZKdOnc7BgwcxcuShxGkzOXzYZ0p+Kdly4ViVlkoYr3JYRAZgh949ufOR29mxTy9eeOYl4qvT2Wnn3gw/5jDIwLfO+i5PPPpUocNMpBwWkYHKuK6KZRGZ3bYflJccZ+Z7LxfF98u3skuuQwjtyS6BPpLsvNbDY4yvpf055ZBcA/TtuxNXXnEhR48aQY8e3Vmw4F0eHv8Y466+gcWLlxQ6PNXjWJWWch+vckmuAbpt15Uzvz6GYaMOZfsderBi+QqmPP9v7r7lXl5/5Y1Ch5dYuSTXUP7Xlcl1eSir5DqE0AYYDxwDLAaOjDG+nI/PKpfkWpKaopyS63JXTsl1uSuW5HrA9gfkJceZ9d6Uovh++VY2NzSGEFoCvyGbWP//9u48SrKqStT4V1DMIvMsiNK4oUBoUQTfQwFpWS0yqggK3YDta58zIg9EkEG0BBxwaGddDS1CC7TK0IooCAsBBxBQQDZI2zZQgCCTzAWV749zIusSlRFVlRGVN7Pq+9WqdSPuELHjRmTmjnP3OeevwOsXVWItSZK0uBoZmdN2CFPaYpNcU2ZffFO9vQzwg4jote8dmbnthEQlSZKkJcbilFw3Rz5Zvv7vxeH4JEmSxjDHofgGstgk15l5GbBE1PJIkiRpclpskmtJkiQNbnEa7KINJteSJEkaZVnIYBbbGRolSZKkiWbLtSRJkkZZFjIYW64lSZKkIbHlWpIkSaPm2HI9EJNrSZIkjRqxQ+NALAuRJEmShsSWa0mSJI2yQ+NgbLmWJEmShsSWa0mSJI1yEpnB2HItSZIkDYkt15IkSRplzfVgTK4lSZI0ynGuB2NZiCRJkjQktlxLkiRplGUhg7HlWpIkSRoSW64lSZI0yqH4BmNyLUmSpFGWhQzGshBJkiRpSGy5liRJ0iiH4huMLdeSJEnSkNhyLUmSpFEjdmgciMm1JEmSRlkWMhjLQiRJkqQhseVakiRJoyb7UHwRsS5wFLA7sAHwMPAr4HOZeUmbsYEt15IkSZoiImIr4Ebg/cCLgUbJWlUAABYJSURBVKeANSmJ9k8i4sMthgeYXEuSJKlhZBH9G1RErACcD6wBXAdsmZmrAKsBnwGmATMjYteBn2wAJteSJEmaCt4JvBB4FNgjM28CyMxHMvNw4AeUBPuT7YVoci1JkqSGkZGRRfJ/CA6oyzMz864xtn+qLreJiBjGE46HHRolSZI0ajJ2aIyIlYGX17s/7rHbLyidG1cBdgFyAkKbhy3XkiRJmuw2p5R8ANw01g6ZOYe5CfWMiQhqLLZcS5IkadTka7cGYL3G7Vl99utsW6/PPouUyfU4PfP0XdPmv5ckSdLUMklznJUat5/os9/jdfm8RRhLX5aFSJIkSUNici1JkqTJ7rHG7RX67LdiXT66CGPpy+RakiRJk12zznr9Pvt1tt29CGPpy+RakiRJk90tzO1rucVYO0TEUkBnfOubJyKosZhcS5IkaVLLzL8C19S7r+ux23aUMa4BLlnkQfVgci1JkqSp4My6PCAixhpq7/C6vDYzW5lABkyuJUmSNDV8DfgTsDJwYUTMgDJ7Y0ScAryx7veRluIDYNpknOJSkiRJ6hYRW1NKPtaoqx6hjGm9FKUm+yOZeVJL4QEm15IkSZpCImJd4Chgd2ADSoL9K+DUzGyt1rrD5FqSJEkaEmuuJUmSpCExuZYkSZKGxORakiRJGhKTa0mSJGlITK4lSZKkITG5lloUEdMiYlrbcUiSpOEwuV6MRMR6EbFPRKzadiyav4hYOjNHMnOk3jfJlgYUEetGxNkR8ff1vn/nJE2o6W0HoMFExLTMHImI44DjgPuBt1JmL9IkFREHAjMiYkXgaeBM4EbgmVYDk6awiDgGOJbyt20V4KLMnNNuVBpLRLwBWAZYCbg2M29pOSRpaJxEZjEQES8Bmr+YvgzMzMxZLYWkHiJib+BkYNOuTXcAX87Mkyc+Ko2lfgH6P8CBmXlH2/Got4jYF/gM8IK66lTg5Mz8c3tRaSwRsQ+lIWgr4AlgBcqU1V8HvpmZ17YYnjQUJteLgYjYAfgepQV0feAe4IPAObbaTA4R8ULgeOCguuqXwHnAusAuwBZ1/RaZ+fsJD1CjImJjSnK2V111XGae2F5E6iUiXkZ5r15TV11Aeb+ur9uX8nfg5BARGwEzgbfVVb8DZgEbAjMof78uAf45M+9qJUhpSKxFWzw8BawJnATcTEnY3sS8raNqQUS8AvgaJbG+F3hrZr4qM0/KzEOBQ4Gr6u7vailMARGxHfB5SmL917r6sIh4aXtRqVtETI+IU4BrKYn1DcBbMnOvzLy+dhSebmI9OUTEJsBnKYn13cD+mbk1sA/wMuBLlJK4nYGDWwpTGhqT68VDpwPjQ5TL2AB7AH8XESu0E5IAImIl4C3ArsDZwEsz87t127J1t8spVxsA5kTE0hMeqDota8cBrwduAj4A/JpSu/uBiLCPyuSxGnN/790G7JqZ5wJExPK1o7D9FyaB2qH0CGBPSsv0dpl5dt08OzNnAycClwLLA1vbKV9Tncn1FNYYXWJ2Xa6cmVcDPwKWA/YFbHFrUWY+BtwJfBH4aGbe30ieZ9c/PMtRrjzUQ/LZFkIVrEFpOXuSUhd/OvDRuu3tlPIdTQKZeR9wGnA95QrdgVC+sGbmk/X2lhGxbUT8r1rqo3YsB+xH6WT6qcy8s/M7MDOfraU7f6Y0MkB5Px9pJ1RpOEyup7DOEG7AenW5XF0eWpevAd4QEauDQ7216DTg45l5G4z+QZleW9fmUEp4tqR0arw4IpZvL9Ql2k3A+4HdMvOM+vP1U8pILgBHRMQqrUWnbjdQ3psR4J0R8aLMfDoiNo+Ib1Peu/OBnwPXRMRXImKn9sJdMmXmE8A/AO/OzIvrurEaEG6hNBQtD6w9cRFKw2eHximsMQzfbsCFlFENzqzbTgEOp/zC+hDwY+sPJ5dasvNJSkIHJVm4h9Ip9Xzg85l5f0vhLdHqF9FpmTknIrYCrqaMavDOzPxGu9GpIyJmUDo0vo4ySlJSauYBHqd8YV0VWIeShD8EvDozb574aJdcEbEMMKfRUj3P36KIeDfwL8CVmfnqCQ9SGiJbrqewRsv1JnW5UmPzscCDwGbA3sDKADURV8siYhfgMeYm1mdROmetQbkScTTwrYjYsJ0Il2yNqwpk5m8pw7xBab3eqL3I1CWBMyhJ89uZm1h/gvJ7cRcggBMoo1OsBpwSEWtNfKhLrsyc3Wmt7k6sG1dUO63Vt9b19j3RlGVyPYU1Zh77S13eVdd36g6PrOv3Bo6OiOuBCyNi/4mNVGPovGffBTbLzAMy8x3Aq5jbW34P4B2Njo9qz5eB2ykJ2z+3HIuqmrBdRhmCb3nKEJfbZOZHM/Ne4N7MfIQyktJR9bDdgL9vIVyNrZNcb1WXN0LP0hFpSjC5nsIaLQAb1+XqdTlSt38T+D2wFqVEZCvgj5QOdmpRHYd3k8x8a2be2pyiOTN/SGl5gzLSyIptxKi5MvMe5r4nh9bhFTUJ1Al+zgL+HTisDsW3VC0/6LSWPpWZPwI6JT3vbilcdamlVysBO9RVF7cZjzQMJtdTWONy2qPAHODPUC7BRcSaEXEssDmlk8gIpY53Rmb+3M6N7cvMP0K5/Nn4otRZnkkp6wlguxbC07z+nTJc2IqUsa87HYibV5HUjl9QJo+5GkrCNkb5wVLANZRZATeMiBdPfJjq4eWUIS8TuLXz98nSEE1V/kGYwho11xtS3sunACLinyiX1o6v2++gXHpbFfjbrmPVsublz8b7sgol0X6MkgyoZbXUama9uz+N0oLa+rZ07bilCZaZD3VG4xmr4aB2/p5D+T24AmXCkr9076eJ1fhSujWwDHBTHfd6en3Pnq377WiDkKYSk+vFwyOURGy7iLiaculzbeAKSsvnjLrfa4C9IsJhjiahzqxy9e7OlM6NDwJ/aC8qNWXmpcC36t0jI2JFgIjYlTI76nFtxaaiu+GgJmVL1b4LO9bVv8Evra1rXF3YqS5/UdfPriNhbRUR3wd+xtwx56VJz+R68fAQ5b08iVJCcCewX2bumJm3ZebTwMfqvu+jtBJoEmmMe/1MRGwJHEAp5/l0Zs5qOTw916nAw8D2wEci4kzgIsrkF3t2Em61r/Fz9SywLfBayiRBZ9Tfi2pZnY1x+3r3/LpuzYj4DHAdsBdliNLr2olQWngm14uHr1M7MQLHZ+ZGmXkOQKMu9ATKL6hvZOZPWohRfXSmao6I91Km3J4BnEsZTUSTQOcSdh0j+Yt19VGUEhGAYzJzq8x8vI34NK/Gz9X7KPXy61CuPFzQZlx6ji0pQ8VeCcyq413fAHyQUsZzfGaun5m+Z5oynERmMVFHL7gnM++s96c3/rBMry2iK9TZsjRJRMR2lJKebYDDKK2fUJK3EzLzgbZi07xqicE/AB8HXlBXfxf4kFcYJo+I2JbSePQyyoy1L6mbPkuZLfWhtmJT0ZgErTN5zN3ALErnRig/V4dn5l1txSiNl8n1Yqb2rp5jh8XJr05LfzVzE2qASyijHlzVTlTqpSZsX6UkbFAm/TksM69oLyp1i4jVKP1NZjRWXw4c7c/V5BMRZwH7NVb9Bjg0M3/eUkjSwKbPfxdNJQ68P3Vk5gMR8QnKWNa/B36amT9uOSz1ti4lsb4POCIzT285Ho0hMx+MiE9RJvu5CTgvM/+z5bDU2611+QBwZGZ+q9/O0lRgy7XUstqh52GvNkx+EXEIcGZmPtV2LOovIl4AzOoe71qTS0S8CNgT+Ko/V1pcmFxLkiRJQ+JoIZIkSdKQmFxLkiRJQ2JyLUmSJA2JybUkSZI0JCbXkiRJ0pCYXEuSJElDYnItSZIkDYnJtSRJkjQkJteSJEnSkJhcS5IkSUNici1JkiQNicm1JEmSNCTT2w5A0qIVEQcD/zrGpjnAw8AfgJ8CX8rMuyYwtHGLiJF685DMPK1r22XAjsDpmXnwxEY2cSLiNOAg4PLM3Gkcx/c8h4MaNLYhxXA8cBzwp8zcuI0YJC2ZbLmWllxLAasB2wJHATdHxBvaDWnyiojLImKkJo6SJI3J5FpasuwGrFz/rwJsCZxCacV+PnB2RGzSXniSJE1tloVIS5YnMvPRxv2bgCMj4nHgeGBF4DDgPS3ENhRtlSFIkgS2XEsqTgaerLd3aTMQSZKmMluuJZGZT0bE7cAWwIbNbd0dBCPi9cC7gFcAawMXZubeXcesRmn93gP4G+B5wH3AFcAXMvPqfvFExJaUOvDXUurC7wF+BJyUmX+az7HPibfPfnsCBwDb19fxBHAncANwDnBBZo40OsZ1HBQRB3U93AmZeXzX408D3ggcCLwSWBN4jHK14CzgG5k5u098KwKHA/sBLwYeBa4DvpiZF/Q8AUMQEdOBVwN7Us7lJpSrGg8Bv6Ocn29l5tML+HivoVwR2R5YlXKefwDMzMwH5nPsQJ8lSZpotlxL6ni2Lqf12iEiPgn8kJLorAcsPcY+O1NGIDmRklSuDiwLbADsD1wVER/r8xxvAn4DvA1YF1gOeCHwf4HrImLbhX1hXY+/ekT8FDgPeAuwEbA8JYl/KSUZPo9Skz7e51iNMgLLucDewPqUc7AasAPwJcp5WLvH8esA1wAnADNqfGsCrwPOj4gTxxvbAnoPcClwKPAySj3+9BrDzsCXgSsiYvX5PVBEvBP4GbAXsA7l/dwE+BDwu4iIPscO9FmSpDaYXEsiIpahtAoCzOqx298BHwYuoLRqrlWP+WLjcbahtDCvTmll3Z+SGK9Oaek+re760Yh4xxhxzADOBJahtFYfRElMNwAOBp4GvjuuF1kef9kaX6f05UxKsrgupfV6e+BoSkLXMZPSAfTn9f53mNsptPN/ZuM5pgP/SWl1/ytwDLAV5Ry8CHgv8CDlfJwTEc/5PVxbvM8BNgdGgM9ROp6uSUnML66PueN4z8MCeIJybv4R2I7yBWRtSqJ9HPAAJdn96nwep/P5uBbYlfKZ2RQ4lvJerg9cEBHLdx846GdJktpiWYgkKC2UK9bbP+uxzwaUxPatmdkZI/l+4PbGPv9KaZn8BbBjV9nAtcAhEXE3peRjZkR8JzOfaOxzCqVl8rF6/K2NbadHxC8prdrjdRglKQT4YGZ+rmv7fcAvI+IkSmJLfQ1PR0SnZf+Zrk6h3d4PvAp4CnhtZl7T2PYg8KWIuAL4FfAaSunIuY193kT58gJwTGbObGy7MiJ2Ay6ifNlZJDLz68DXx9h0H3B9RHwPuB54c0Rskpm3j7EvlM/MDcBOmfl4XXc/cGJE/BdwBiXZfg/wma5jB/0sSVIrbLmWllARsVREbBgRRwOfqKtnA6f2OORZ4LBGYt39eDtTWmihTEzSqx73REryvBalNbNz/DrA6+vdf+lKrAHIzFsoJRXj9f66vHyMxLr5PHN6vc6FeI7PdSXWzcf/LaVlGErdd9PBdXkX5ctG97HPAh8cZ2xDkZk3Ur7kTGP+Sf6HG4l18zG+Q/mCAXBIc9ugnyVJapMt19KS5Wd9SlyfAt6emTf32H59ZvYqGYG5Sdb/AHdGxPP67HsL8HLK5f3z6rpXMfcL//f7HPs9Ske/hVJLTtard09f2OMX8Dk2pZQuAFw+n3Pwu7p8ReP4acD/rncvyMxnxjowM2+MiFuBlwwYck819ncAu1PKUlajXFXo1i+Gx4Cf9Nn+fcqVhBkRsWpmPlTXD/pZkqTWmFxLS7ZnKGUdl1BGXsg++/7XfB6rk7VvRKk1XhBrNW5v3Lh9S59jfr+Aj92tOTnO9eN8jPlpfnP54QIe0zwHq1BG04D+5wDKeVgkyXVEbE4pPdloAXbv1/HzttrS3kvnNU6rz9VJrgf9LElSa0yupSXLbpQhzADmjHW5vo/57Tue0TWaHdmarZP9apr7bevn+Y3bC5qwLazxnIPlGrcX9BwsyPZxqR0y/4OS2D5KKRO6GPhvynnrJMsXUVrZ+/0dWZjXsHLj9qCfJUlqjcm1tGTpnqFxmDqP+6vM3G6A46EkmQ/32K9fiUA/zYR65Z57Dab5GmZk5sK2snefg37Gex7mZ0fKSCUAb87MH4+103xKNToW5jU0359BP0uS1Bo7NEoalk7ZyItr7fDC+u/G7c367Ld5n239NIfX+9txPsb8NEtnNum5V28PM7c0ot85gPGfh/nZui4f7JNYL8tzS2B62TQi5hkLvaHzGkco9dUdg36WJKk1JteShuXiulyTMsbzwroamFNv79NnvzeO47GpHTU7HTL/cRwP0ZlNsV+yeCNwd72938I+QR2h5Mp6d/daojGPiNiCRdeZsVOm0u91vpEFK8NYiTLxTS+dmT1vbnRmhME/S5LUGpNrScNyMSW5BPhKHVqvp4jYOCJG640z817KpCEA742IeZLHiNgMePcAMX6hLneKiPf1iW2pMVpM/1KX6/c6ribHn6133xYRb+4XTEQsHxEv7Fp9Wl2+ADhijGOWpvdwicPwx7p8fkTMM1FNRKzLGEME9nFSncq9+3HeRpmgBsqY1k0DfZYkqU3WXEsaiswciYiDKDMZbgrcEBGfpnR8u4sy6+L6lCHT9qKMab0uZQjAjiMoLZ0rAZdFxJGUacShjGN8MnAvZabD8TiV0ur6SuALEfFK4JuUkTfmUEYseS3wT5TEr9maei2lNXqHiNiXMsLKI3XbnMzstLp/HtiTMhHM2RFxGvDt+hxPUWYY3KK+zv0oieqnG8/zH5ROp68GPh4RawLfqK97M8oMia+jlNFsPM7z0M9FlPrnlYGzIuL/AZdTzs/OlDHR1wD+xNxhB3uZRZm+/bKI+AhlpsVVKVPbH1P3uY2uscuH9FmSpFbYci1paDLzN5Qxiu8C1gE+RRnP+QFKcngdJVHcvR7ybNfxN1MmVZlNGZP63ygJ2ixKi+5yjKPcovH4T1NGTLm8rjoQuKzGdh/wa0oCP1bJxbcpMywuC5xNacmeXf8f23iO2fX1nUcZYu4Q4FJKucgDlNrv8yjToK9FmQa8GeMIsC8lGZ9GmTDm5vp8V1K+ZMxsvIahquUZ76Ik0+tRZlG8g/KenkFJYg/huTXyvdwGfIAyDvVPKLMz/gH4GOU8zgL2yMwnx4hjoM+SJLXF5FrSUGXmVZTk9H2UhOpeSgL6BKXk4ALKdOsbZeaDYxx/LrANcBZwDyX5/B9KC/PLM/PXA8b3F0oL7L7ADygJ3tOU5PW3wNcordcPdx13D2Wim2/XeHrNGkhmPpKZewO7UCasuZ0yocpsShJ/FSVZ3CEzvzDG8fdSWmWPpyTZT9b4LgX2ycyjx/XiF1CdPXFnSpnOQ8x9D/4N2C4zz16Ix/oKJUm+EPgzpXX5dkr5zEv7ja0+6GdJktowbWRkvDP8SpIkSWqy5VqSJEkaEpNrSZIkaUhMriVJkqQhMbmWJEmShsTkWpIkSRoSk2tJkiRpSEyuJUmSpCExuZYkSZKGxORakiRJGhKTa0mSJGlITK4lSZKkITG5liRJkobE5FqSJEkaEpNrSZIkaUhMriVJkqQhMbmWJEmShsTkWpIkSRoSk2tJkiRpSEyuJUmSpCH5//9OBy1+zMiqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 270,
       "width": 363
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
    "hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
    "hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6Feyo0ULAmEW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "review_body: This is just OK. \\star_rating: 5\n",
      "\n",
      "review_body: This sucks. \\star_rating: 5\n",
      "\n",
      "review_body: This is great. \\star_rating: 5\n",
      "\n",
      "review_body: This is just OK. \\star_rating: 5\n",
      "\n",
      "review_body: This sucks. \\star_rating: 5\n",
      "\n",
      "review_body: This is great. \\star_rating: 5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sentences = [\n",
    "  \"This is just OK.\",\n",
    "  \"This sucks.\",\n",
    "  \"This is great.\"\n",
    "]\n",
    "\n",
    "pred_tokens = map(tokenizer.tokenize, sentences)\n",
    "pred_tokens = map(lambda tok: [\"[CLS]\"] + tok + [\"[SEP]\"], pred_tokens)\n",
    "pred_token_ids = list(map(tokenizer.convert_tokens_to_ids, pred_tokens))\n",
    "\n",
    "pred_token_ids = map(lambda tids: tids +[0]*(features.max_seq_len-len(tids)),pred_token_ids)\n",
    "pred_token_ids = np.array(list(pred_token_ids))\n",
    "\n",
    "predictions = model.predict(pred_token_ids).argmax(axis=-1)\n",
    "\n",
    "for review_body, star_rating in zip(sentences, predictions):\n",
    "  print(\"review_body:\", review_body, \"\\star_rating:\", classes[star_rating])\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1npbTSBRj0Bt"
   },
   "source": [
    "# References\n",
    "\n",
    "- https://www.kdnuggets.com/2020/02/intent-recognition-bert-keras-tensorflow.html\n",
    "- https://mccormickml.com/2019/07/22/BERT-fine-tuning/\n",
    "- https://github.com/snipsco/nlu-benchmark/tree/master/2017-06-custom-intent-engines\n",
    "- https://jalammar.github.io/illustrated-bert/\n",
    "- https://towardsdatascience.com/bert-for-dummies-step-by-step-tutorial-fb90890ffe03\n",
    "- https://www.reddit.com/r/MachineLearning/comments/ao23cp/p_how_to_use_bert_in_kaggle_competitions_a/"
   ]
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